COMPARISON OF UNCALIBRATED RGBVI WITH SPECTROMETER-BASED NDVI DERIVED FROM UAV SENSING SYSTEMS ON FIELD SCALE

Journal:ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences

Volume:XLI-B8

Year:2016

Pages:837--843

Abstract

<p>The development of UAV-based sensing systems for agronomic applications serves the improvement of crop management. The latter is in the focus of precision agriculture which intends to optimize yield, fertilizer input, and crop protection. Besides, in some cropping systems vehicle-based sensing devices are less suitable because ﬁelds cannot be entered from certain growing stages onwards. This is true for rice, maize, sorghum, and many more crops. Consequently, UAV-based sensing approaches ﬁll a niche of very high resolution data acquisition on the ﬁeld scale in space and time. While mounting RGB digital compact cameras to low-weight UAVs (< 5 kg) is well established, the miniaturization of sensors in the last years also enables hyperspectral data acquisition from those platforms. From both, RGB and hyperspectral data, vegetation indices (VIs) are computed to estimate crop growth parameters. In this contribution, we compare two diﬀerent sensing approaches from a low-weight UAV platform (< 5 kg) for monitoring a nitrogen ﬁeld experiment of winter wheat and a corresponding farmers' ﬁeld in Western Germany. (i) A standard digital compact camera was ﬂown to acquire RGB images which are used to compute the RGBVI and (ii) NDVI is computed from a newly modiﬁed version of the Yara N-Sensor. The latter is a well-established tractor-based hyperspectral sensor for crop management and is available on the market since a decade. It was modiﬁed for this study to ﬁt the requirements of UAV-based data acquisition. Consequently, we focus on three objectives in this contribution: (1) to evaluate the potential of the uncalibrated RGBVI for monitoring nitrogen status in winter wheat, (2) investigate the UAV-based performance of the modiﬁed Yara N-Sensor, and (3) compare the results of the two diﬀerent UAV-based sensing approaches for winter wheat.</p>